Investigation of Direct Electron Transfer of Glucose Oxidase on a Graphene-CNT Composite Surface: A Molecular Dynamics Study Based on Electrochemical Experiments.

Graphene and its variants exhibit excellent electrical properties for the construction of enzymatic interfaces. In particular, the direct electron transfer of glucose oxidase on the electrode surface is a very important issue in the development of enzyme-based bioelectrodes. However, the number of studies conducted to assess how pristine graphene forms different interfaces with other carbon materials is insufficient. Enzyme-based electrodes (formed using carbon materials) have been extensively applied because of their low manufacturing costs and easy production techniques. In this study, the characteristics of a single-walled carbon nanotube/graphene-combined enzyme interface are analyzed at the atomic level using molecular dynamics simulations. The morphology of the enzyme was visualized using an elastic network model by performing normal-mode analysis based on electrochemical and microscopic experiments. Single-carbon electrodes exhibited poorer electrical characteristics than those prepared as composites with enzymes. Furthermore, the composite interface exhibited 4.61- and 2.45-fold higher direct electron efficiencies than GOx synthesized with single-carbon nanotubes and graphene, respectively. Based on this study, we propose that pristine graphene has the potential to develop glucose oxidase interfaces and carbon-nanotube-graphene composites for easy fabrication, low cost, and efficient electrode structures for enzyme-based biofuel cells.

A study of forecasting the Nephila clavipes silk fiber's ultimate tensile strength using machine learning strategies.

Recent advancements in biomaterial research conduct artificial intelligence for predicting diverse material properties. However, research predicting the mechanical properties of biomaterial based on amino acid sequences have been notably absent. This research pioneers the use of classification models to predict ultimate tensile strength from silk fiber amino acid sequences, employing logistic regression, support vector machines with various kernels, and a deep neural network (DNN). Remarkably, the model demonstrates a high accuracy of 0.83 during the generalization test. The study introduces an innovative approach to predicting biomaterial mechanical properties beyond traditional experimental methods. Recognizing the limitations of conventional linear prediction models, the research emphasizes the future trajectory toward DNNs that can adeptly capture non-linear relationships with high precision. Moreover, through comprehensive performance comparisons among diverse prediction models, the study offers insights into the effectiveness of specific models for predicting the mechanical properties of certain materials. In conclusion, this study serves as a pioneering contribution, laying the groundwork for future endeavors and advocating for the seamless integration of AI methodologies into materials research.

Profiling of BCLxL Protein Complexes in Non-Small Cell Lung Cancer Cells via Multiplexed Single-Molecule Pull-Down and Co-Immunoprecipitation.

We introduce multiplexed single-molecule pull-down and co-immunoprecipitation, named m-SMPC, an analysis tool for profiling multiple protein complexes within a single reaction chamber using single-molecule fluorescence imaging. We employed site-selective conjugation of biotin and fluorescent dye directly onto the monoclonal antibodies, which completed an independent sandwich immunoassay without the issue of host cross-reactivity. We applied this technique to profile endogenous B-cell lymphoma extra-large (BCLxL) complexes in non-small cell lung cancer (NSCLC) cells. Up to three distinct BCLxL complexes were successfully detected simultaneously within a single reaction chamber without fluorescence signal crosstalk. Notably, the NSCLC cell line EBC-1 exhibited high BCLxL-BAX and BCLxL-BAK levels, which closely paralleled a strong response to the BCLxL inhibitor A-1331852. This streamlined method offers the potential for quantitative biomarkers derived from protein complex profiling, paving the way for their application in protein complex-targeted therapies.

High-speed measurements of SNARE-complexin interactions using magnetic tweezers.

In neuroscience, understanding the mechanics of synapses, especially the function of force-sensitive proteins at the molecular level, is essential. This need emphasizes the importance of precise measurement of synaptic protein interactions. Addressing this, we introduce high-resolution magnetic tweezers (MT) as a novel method to probe the mechanics of synapse-related proteins with high precision. We demonstrate this technique through studying SNARE-complexin interactions, crucial for synaptic transmission, showcasing its capability to apply specific forces to individual molecules. Our results reveal that high-resolution MT provides in-depth insights into the stability and dynamic transitions of synaptic protein complexes. This method is a significant advancement in synapse biology, offering a new tool for researchers to investigate the impact of mechanical forces on synaptic functions and their implications for neurological disorders.

Enhancing pediatric pneumonia diagnosis through masked autoencoders.

Pneumonia, an inflammatory lung condition primarily triggered by bacteria, viruses, or fungi, presents distinctive challenges in pediatric cases due to the unique characteristics of the respiratory system and the potential for rapid deterioration. Timely diagnosis is crucial, particularly in children under 5, who have immature immune systems, making them more susceptible to pneumonia. While chest X-rays are indispensable for diagnosis, challenges arise from subtle radiographic findings, varied clinical presentations, and the subjectivity of interpretations, especially in pediatric cases. Deep learning, particularly transfer learning, has shown promise in improving pneumonia diagnosis by leveraging large labeled datasets. However, the scarcity of labeled data for pediatric chest X-rays presents a hurdle in effective model training. To address this challenge, we explore the potential of self-supervised learning, focusing on the Masked Autoencoder (MAE). By pretraining the MAE model on adult chest X-ray images and fine-tuning the pretrained model on a pediatric pneumonia chest X-ray dataset, we aim to overcome data scarcity issues and enhance diagnostic accuracy for pediatric pneumonia. The proposed approach demonstrated competitive performance an AUC of 0.996 and an accuracy of 95.89% in distinguishing between normal and pneumonia. Additionally, the approach exhibited high AUC values (normal: 0.997, bacterial pneumonia: 0.983, viral pneumonia: 0.956) and an accuracy of 93.86% in classifying normal, bacterial pneumonia, and viral pneumonia. This study also investigated the impact of different masking ratios during pretraining and explored the labeled data efficiency of the MAE model, presenting enhanced diagnostic capabilities for pediatric pneumonia.

Unraveling the Mechanical Property Decrease of Electrospun Spider Silk: A Molecular Dynamics Simulation Study.

This study investigated the impact of electric fields on Nephila clavipes spider silk using molecular dynamics modeling. Electric fields with varying amplitudes and directions were observed to disrupt the ß sheet structure of spider silk and reduce its mechanical properties. However, a notable exception was observed when a 0.1 V/nm electric field was applied in the antiparallel direction, resulting in improvements in Young's modulus and ultimate tensile strength. The antiparallel direction was observed to be particularly sensitive to electric fields, causing disruptions in beta sheets and hydrogen bonds, which significantly influence the mechanical properties. This study demonstrates that spider silk maintains its structural integrity at 0.1 V/nm. Possibly, lowering the power levels of typical electrospinning machines can prevent secondary structural disruption. These findings provide valuable insights for enhancing silk fiber production and applications using natural silk proteins while shedding light on the impact of electric fields on other silk proteins. Finally, this study opens up possibilities for optimizing electrospinning processes to enhance performance in various silk electrospinning applications.

An atomistic scale simulation study of structural properties in the silk-fibrohexamerin complex.

The use of Bombyx mori silk fibroin in composite materials has been extensively explored in many studies, owing to its remarkable mechanical properties. Recently, the N-glycan-engineered P25 protein was utilized to improve the mechanical properties of silk. However, the mechanism by which N-glycan-engineered P25 protein enhances the mechanical properties of silk remains unclear. This study analyzed the interaction between the P25 protein and silkworm silk using quantum mechanics/molecular mechanics multiscale simulations and discovered stronger hydrogen bonding between the amorphous domain and the P25 protein. The results confirmed that glycoengineering of the mannose molecule in N-glycan in orders of three, five, and seven increased the hydrogen bonding of the amorphous structures. However, P25 has fewer binding interactions with the crystalline domain. Silk amino acids and mannose molecules were analyzed using QM simulations, and hydroxyl and charged amino acids in the amorphous domains were found to have relatively higher reactivity with mannose molecules in N-glycans than basic and aliphatic amino acids in the crystalline domain. This study demonstrates how the N-glycan-engineered P25 protein can improve the mechanical properties of silk fibroin and identifies a key factor for N-glycan-engineered proteins.

Biochemical mechanism involved in the enhancement of the Young's modulus of silk by the SpiCE protein.

Silk fibers are known for their superior mechanical properties, with the strongest possessing over seven times the toughness of kevlar. Recently, low molecular weight non-spidroin protein, spider-silk constituting element (SpiCE), has been reported to enhance the mechanical properties of silk; however, its specific action mechanism has not yet been elucidated. Here, we explored the mechanism by which SpiCE strengthened the mechanical properties of major ampullate spidroin 2 (MaSp2) silk through hydrogen bonds and salt bridges of the silk structure via all-atom molecular dynamics simulations. Tensile pulling simulation on silk fiber with SpiCE protein revealed that the SpiCE protein enhanced the Young's modulus by up to 40% more than that of the wild type. Bond characteristic analysis revealed that SpiCE and MaSp2 formed more hydrogen bonds and salt bridges than the MaSp2 wild-type model. Sequence analysis of MaSp2 silk fiber and SpiCE protein revealed that SpiCE protein contained more amino acids that could act as hydrogen bond acceptors/donors and salt bridge partners. Our results provide insights into the mechanism by which non-spidroin proteins strengthen the properties of silk fibers and lay the groundwork for the development of material selection criteria for the design of de novo artificial silk fibers.

High-Speed Magnetic Tweezers for Nanomechanical Measurements on Force-Sensitive Elements.

Single-molecule magnetic tweezers (MTs) have served as powerful tools to forcefully interrogate biomolecules, such as nucleic acids and proteins, and are therefore poised to be useful in the field of mechanobiology. Since the method commonly relies on image-based tracking of magnetic beads, the speed limit in recording and analyzing images, as well as the thermal fluctuations of the beads, has long hampered its application in observing small and fast structural changes in target molecules. This article describes detailed methods for the construction and operation of a high-resolution MT setup that can resolve nanoscale, millisecond dynamics of biomolecules and their complexes. As application examples, experiments with DNA hairpins and SNARE complexes (membrane-fusion machinery) are demonstrated, focusing on how their transient states and transitions can be detected in the presence of piconewton-scale forces. We expect that high-speed MTs will continue to enable high-precision nanomechanical measurements on molecules that sense, transmit, and generate forces in cells, and thereby deepen our molecular-level understanding of mechanobiology.

Predicting mechanical properties of silk from its amino acid sequences via machine learning.

The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, is that their mechanical properties are significantly dependent on the amino acid sequence. Numerous studies have been conducted to determine the specific relationship between the amino acid sequence of silk and its mechanical properties. Still, the relationship between the amino acid sequence of silk and its mechanical properties is yet to be clarified. Other fields have adopted machine learning (ML) to establish a relationship between the inputs, such as the ratio of different input material compositions and the resulting mechanical properties. We have proposed a method to convert the amino acid sequence into numerical values for input and succeeded in predicting the mechanical properties of silk from its amino acid sequences. Our study sheds light on predicting mechanical properties of silk fiber from respective amino acid sequences.

Bimodal CNN for cardiovascular disease classification by co-training ECG grayscale images and scalograms.

This study aimed to develop a bimodal convolutional neural network (CNN) by co-training grayscale images and scalograms of ECG for cardiovascular disease classification. The bimodal CNN model was developed using a 12-lead ECG database collected from Chapman University and Shaoxing People's Hospital. The preprocessed database contains 10,588 ECG data and 11 heart rhythms labeled by a specialist physician. The preprocessed one-dimensional ECG signals were converted into two-dimensional grayscale images and scalograms, which are fed simultaneously to the bimodal CNN model as dual input images. The proposed model aims to improve the performance of CVDs classification by making use of ECG grayscale images and scalograms. The bimodal CNN model consists of two identical Inception-v3 backbone models, which were pre-trained on the ImageNet database. The proposed model was fine-tuned with 6780 dual-input images, validated with 1694 dual-input images, and tested on 2114 dual-input images. The bimodal CNN model using two identical Inception-v3 backbones achieved best AUC (0.992), accuracy (95.08%), sensitivity (0.942), precision (0.946) and F1-score (0.944) in lead II. Ensemble model of all leads obtained AUC (0.994), accuracy (95.74%), sensitivity (0.950), precision (0.953), and F1-score (0.952). The bimodal CNN model showed better diagnostic performance than logistic regression, XGBoost, LSTM, single CNN model training with grayscale images alone or with scalograms alone. The proposed bimodal CNN model would be of great help in diagnosing cardiovascular diseases.

Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases.

BACKGROUND: Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Deep learning methods have been widely used in the field of medical image analysis and have shown promising results in the diagnosis of CVDs. METHODS: Experiments were performed on 12-lead electrocardiogram (ECG) databases collected by Chapman University and Shaoxing People's Hospital. The ECG signal of each lead was converted into a scalogram image and an ECG grayscale image and used to fine-tune the pretrained ResNet-50 model of each lead. The ResNet-50 model was used as a base learner for the stacking ensemble method. Logistic regression, support vector machine, random forest, and XGBoost were used as a meta learner by combining the predictions of the base learner. The study introduced a method called multi-modal stacking ensemble, which involves training a meta learner through a stacking ensemble that combines predictions from two modalities: scalogram images and ECG grayscale images. RESULTS: The multi-modal stacking ensemble with a combination of ResNet-50 and logistic regression achieved an AUC of 0.995, an accuracy of 93.97%, a sensitivity of 0.940, a precision of 0.937, and an F1-score of 0.936, which are higher than those of LSTM, BiLSTM, individual base learners, simple averaging ensemble, and single-modal stacking ensemble methods. CONCLUSION: The proposed multi-modal stacking ensemble approach showed effectiveness for diagnosing CVDs.

A novel selective spleen tyrosine kinase inhibitor SKI-O-703 (cevidoplenib) ameliorates lupus nephritis and serum-induced arthritis in murine models.

Spleen tyrosine kinase (Syk) plays a pivotal role in the activation of B cells and innate inflammatory cells by transducing immune receptor-triggered signals. Dysregulated activity of Syk is implicated in the development of antibody-mediated autoimmune diseases including systemic lupus erythematosus (SLE) and rheumatoid arthritis, but the effect of Syk inhibition on such diseases remains to be fully evaluated. We have developed a novel selective Syk inhibitor, SKI-O-592, and its orally bioavailable salt form, SKI-O-703 (cevidoplenib). To examine the efficacy of SKI-O-703 on the progression of SLE, New Zealand black/white mice at the autoimmunity-established phase were administrated orally with SKI-O-703 for 16 weeks. Levels of IgG autoantibody, proteinuria, and glomerulonephritis fell significantly, and this was associated with hypoactivation of follicular B cells via the germinal center. In a model of serum-transferred arthritis, SKI-O-703 significantly ameliorated synovitis, with fewer neutrophils and macrophages infiltrated into the synovial tissue. This effect was recapitulated when mice otherwise refractory to anti-TNF therapy were treated by TNF blockade combined with a suboptimal dose of SKI-O-703. These results demonstrate that the novel selective Syk inhibitor SKI-O-703 attenuates the progression of autoantibody-mediated autoimmune diseases by inhibiting both autoantibody-producing and autoantibody-sensing cells.

Enantioselective sensing by collective circular dichroism.

Quantitative determination and in situ monitoring of molecular chirality at extremely low concentrations is still challenging with simple optics because of the molecular-scale mismatch with the incident light wavelength. Advances in spectroscopy1-4 and nanophotonics have successfully lowered the detection limit in enantioselective sensing, as it can bring the microscopic chiral characteristics of molecules into the macroscopic scale5-7 or squeeze the chiral light into the subwavelength scale8-17. Conventional nanophotonic approaches depend mainly on the optical helicity density8,9 by localized resonances within an individual structure, such as localized surface plasmon resonances (LSPRs)10-16 or dielectric Mie resonances17. These approaches use the local chiral hotspots in the immediate vicinity of the structure, whereas the handedness of these hotspots varies spatially. As such, these localized resonance modes tend to be error-prone to the stochasticity of the target molecular orientations, vibrations and local concentrations18,19. Here we identified enantioselective characteristics of collective resonances (CRs)20 arising from assembled 2D crystals of isotropic, 432-symmetric chiral gold nanoparticles (helicoids)21,22. The CRs exhibit a strong and uniform chiral near field over a large volume above the 2D crystal plane, resulting from the collectively spinning, optically induced dipoles at each helicoid. Thus, energy redistribution by molecular back action on the chiral near field shifts the CRs in opposite directions, depending on the handedness of the analyte, maximizing the modulation of the collective circular dichroism (CD).

Impact of Pediatric Alopecia Areata on Quality of Life of Patients and Their Family Members: A Nationwide Multicenter Questionnaire Study.

BACKGROUND: Pediatric alopecia areata (AA) can affect the quality of life (QoL) of patients and their family members. Research on the QoL and burden on family members in pediatric AA is limited. OBJECTIVE: This nationwide multicenter questionnaire study described the QoL and burden of the family members of patients with pediatric AA. METHODS: This nationwide multicenter questionnaire study enrolled AA patients between the ages of 5 and 18 years from March 1, 2017 to February 28, 2018. Enrolled patients and their parents completed the modified Children's Dermatology Life Quality Index (CDLQI) and the modified Dermatitis Family Impact (mDFI). The disease severity was measured using the Severity of Alopecia Tool (SALT) survey scores. RESULTS: A total of 268 patients with AA from 22 hospitals participated in this study. Our study found that the efficacy and satisfaction of previous treatments of AA decreased as the severity of the disease increased. The use of home-based therapies and traditional medicines increased with the increasing severity of the disease, but the efficacy felt by patients was limited. CDLQI and mDFI scores were higher in patients with extensive AA than those with mild to moderate AA. The economic and time burden of the family members also increased as the severity of the disease increased. CONCLUSION: The severity of the AA is indirectly proportional to the QoL of patients and their family members and directly proportional to the burden. Physicians need to understand these characteristics of pediatric AA and provide appropriate intervention to patients and their family members.

Tension exerted on cells by magnetic nanoparticles regulates differentiation of human mesenchymal stem cells.

Cells can 'sense' physical cues in the surrounding microenvironment and 'react' by changing their function. Previous studies have focused on regulating the physical properties of the matrix, such as stiffness and topography, thus changing the tension 'felt' by the cell as a result. In this study, by directly applying a quantified magnetic force to the cell, a correlation between differentiation and tension was shown. The magnetic force, quantified by magnetic tweezers, was applied by incorporating magnetotactic bacteria-isolated magnetic nanoparticles (MNPs) in human mesenchymal stem cells. As the applied tension increased, the expression levels of osteogenic differentiation marker genes and proteins were proportionally upregulated. Additionally, the translocation of YAP and RUNX2, deformation of nucleus, and activation of the MAPK signaling pathway were observed in tension-based osteogenic differentiation. Our findings provide a platform for the quantitative control of tension, a key factor in stem cell differentiation, between cells and the matrix using MNPs. Furthermore, these findings improve the understanding of osteogenic differentiation by mechanotransduction.

Evolutionary balance between foldability and functionality of a glucose transporter.

Despite advances in resolving the structures of multi-pass membrane proteins, little is known about the native folding pathways of these complex structures. Using single-molecule magnetic tweezers, we here report a folding pathway of purified human glucose transporter 3 (GLUT3) reconstituted within synthetic lipid bilayers. The N-terminal major facilitator superfamily (MFS) fold strictly forms first, serving as a structural template for its C-terminal counterpart. We found polar residues comprising the conduit for glucose molecules present major folding challenges. The endoplasmic reticulum membrane protein complex facilitates insertion of these hydrophilic transmembrane helices, thrusting GLUT3's microstate sampling toward folded structures. Final assembly between the N- and C-terminal MFS folds depends on specific lipids that ease desolvation of the lipid shells surrounding the domain interfaces. Sequence analysis suggests that this asymmetric folding propensity across the N- and C-terminal MFS folds prevails for metazoan sugar porters, revealing evolutionary conflicts between foldability and functionality faced by many multi-pass membrane proteins.

High-Resolution Single-Molecule Magnetic Tweezers.

Single-molecule magnetic tweezers deliver magnetic force and torque to single target molecules, permitting the study of dynamic changes in biomolecular structures and their interactions. Because the magnetic tweezer setups can generate magnetic fields that vary slowly over tens of millimeters-far larger than the nanometer scale of the single molecule events being observed-this technique can maintain essentially constant force levels during biochemical experiments while generating a biologically meaningful force on the order of 1-100 pN. When using bead-tether constructs to pull on single molecules, smaller magnetic beads and shorter submicrometer tethers improve dynamic response times and measurement precision. In addition, employing high-speed cameras, stronger light sources, and a graphics programming unit permits true high-resolution single-molecule magnetic tweezers that can track nanometer changes in target molecules on a millisecond or even submillisecond time scale. The unique force-clamping capacity of the magnetic tweezer technique provides a way to conduct measurements under near-equilibrium conditions and directly map the energy landscapes underlying various molecular phenomena. High-resolution single-molecule magnetic tweezerscan thus be used to monitor crucial conformational changes in single-protein molecules, including those involved in mechanotransduction and protein folding.